Constructing gazetteers from volunteered Big Geo-Data based on Hadoop

Song Gao, Linna Li, WenWen Li, Krzysztof Janowicz, Yue Zhang

Research output: Contribution to journalArticlepeer-review

115 Scopus citations


Traditional gazetteers are built and maintained by authoritative mapping agencies. In the age of Big Data, it is possible to construct gazetteers in a data-driven approach by mining rich volunteered geographic information (VGI) from the Web. In this research, we build a scalable distributed platform and a high-performance geoprocessing workflow based on the Hadoop ecosystem to harvest crowd-sourced gazetteer entries. Using experiments based on geotagged datasets in Flickr, we find that the MapReduce-based workflow running on the spatially enabled Hadoop cluster can reduce the processing time compared with traditional desktop-based operations by an order of magnitude. We demonstrate how to use such a novel spatial-computing infrastructure to facilitate gazetteer research. In addition, we introduce a provenance-based trust model for quality assurance. This work offers new insights on enriching future gazetteers with the use of Hadoop clusters, and makes contributions in connecting GIS to the cloud computing environment for the next frontier of Big Geo-Data analytics.

Original languageEnglish (US)
Pages (from-to)172-186
Number of pages15
JournalComputers, Environment and Urban Systems
StatePublished - Jan 1 2017


  • Big Geo-Data
  • CyberGIS
  • Gazetteers
  • Hadoop
  • Scalable geoprocessing workflow
  • Volunteered geographic information

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Ecological Modeling
  • General Environmental Science
  • Urban Studies


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